PROBLEM STATEMENT

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

ALGORITHMS

XGBoost, Matrix Factorization, Support Vector, KNN Base Line Models.

OBJECTIVES

• Predict the rating that a user would give to a movie that he ahs not yet rated.

• Minimize the difference between predicted and actual rating (RMSE and MAPE).

DATA FORMAT

• Each line in the file corresponds to a rating from a customer and its date in the following format:

  CustomerID,Rating,Date

• MovieIDs range from 1 to 17770 sequentially. CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users. Ratings are on a five star (integral) scale from 1 to 5. Dates have the format YYYY-MM-DD.

TYPE OF MACHINE LEARNING PROBLEM

For a given movie and user we need to predict the rating would be given by him/her to the movie. The given problem is a Recommendation problem It can also be converted as a Regression problem.

PERFORMANCE METRIC

• Mean Absolute Percentage Error

• Root Mean Square Error

MACHINE LEARNING OBJECTIVE and CONSTRAINTS

• Minimize RMSE.

• Try to provide some interpretability.

EXPLORATORY DATA ANALYSIS

Preprocessing

• Spliting data into Train and Test(80:20)
• Exploratory Data Analysis on Train data
• Distribution of ratings
• Number of Ratings per a month
• Analysis on the Ratings given by user
• Analysis of ratings of a movie given by a user
• number of ratings on each day of the week
• Creating sparse matrix from data frame
• Finding Global average of all movie ratings, Average rating per user, and Average rating per movie
• Computing Similarity matrices
• Computing User-User Similarity matrix

Training the model

• Trying with all dimensions (17k dimensions per user)
• Trying with reduced dimensions (Using TruncatedSVD for dimensionality reduction of user vector)
• Computing Movie-Movie Similarity matrix
• Finding most similar movies using similarity matrix

Machine Learning Steps

• Sampling Data
• Build sample train data from the train data
• Build sample test data from the test data
• Finding Global Average of all movie ratings, Average rating per User, and Average rating per Movie (from sampled train)
• Featurizing data
• Featurizing data for regression problem

Featurizing train and test data

• GAvg : Average rating of all the ratings

• Similar users rating of this movie: sur1, sur2, sur3, sur4, sur5 ( top 5 similar users who rated that movie.. )

• Similar movies rated by this user: smr1, smr2, smr3, smr4, smr5 ( top 5 similar movies rated by this movie.. )

• UAvg : User’s Average rating

• MAvg : Average rating of this movie

• rating : Rating of this movie by this user.

This problem can be solved using following machine learning techniques.

• Recommendation Model
• Regression Model

RECOMMENDATION MODEL

Transforming data for Surprise models:

Transforming train data:

• We can’t give raw data (movie, user, rating) to train the model in Surprise library.

• They have a saperate format for TRAIN and TEST data, which will be useful for training the models like SVD, KNNBaseLineOnly….etc..,in Surprise.

• Form the trainset from a file, or from a Pandas DataFrame.

Transforming test data:

• Testset is just a list of (user, movie, rating) tuples. (Order in the tuple is impotant)

Applying Machine Learning model.

• Global dictionary that stores rmse and mape for all the models….

  It stores the metrics in a dictionary of dictionaries

    keys : model names(string)
  
    value: dict(key : metric, value : value )
  

XGBoost with initial 13 features

TEST DATA

RMSE : 1.0890322448240302

MAPE : 35.13968692492444

Suprise BaselineModel

TEST DATA

RMSE : 1.0865215481719563

MAPE : 34.9957270093008

XGBoost with initial 13 features + Surprise Baseline predictor

TEST DATA

RMSE : 1.0891181427027241

MAPE : 35.13135164276489

Surprise KNNBaseline with user user similarities

Test Data

RMSE : 1.0865005562678032

MAPE : 35.02325234274119

Surprise KNNBaseline with movie movie similarities

Test Data

RMSE : 1.0868914468761874

MAPE : 35.02725521759712

XGBoost with initial 13 features + Surprise Baseline predictor + KNNBaseline predictor

• First we will run XGBoost with predictions from both KNN’s ( that uses User_User and Item_Item similarities along with our previous features.
• Then we will run XGBoost with just predictions form both knn models and preditions from our baseline model.

TEST DATA

RMSE : 1.088749005744821

MAPE : 35.188974153659295

REGRESSION MODEL

Matrix Factorization Techniques

SVD Matrix Factorization User Movie intractions

Test Data

RMSE : 1.0860031195730506

MAPE : 34.94819349312387

SVD Matrix Factorization with implicit feedback from user ( user rated movies )

Test Data

RMSE : 1.0862780572420558

MAPE : 34.909882014758175

XgBoost with 13 features + Surprise Baseline + Surprise KNNbaseline + MF Techniques

TEST DATA

RMSE : 1.0891599523508655

MAPE : 35.12646240961147

XgBoost with Surprise Baseline + Surprise KNNbaseline + MF Techniques

TEST DATA

RMSE : 1.095123189648495

MAPE : 35.54329712868095